Plotly

Let’s make some plotly plots

library(tidyverse)
library(httr)
library(jsonlite)
library(plotly)

get_all_inspections = function(url) {
  
  all_inspections = vector("list", length = 0)
  
  loop_index = 1
  chunk_size = 50000
  DO_NEXT = TRUE
  
  while (DO_NEXT) {
    message("Getting data, page ", loop_index)
    
    all_inspections[[loop_index]] = 
      GET(url,
          query = list(`$order` = "zipcode",
                       `$limit` = chunk_size,
                       `$offset` = as.integer((loop_index - 1) * chunk_size)
                       )
          ) %>%
      content("text") %>%
      fromJSON() %>%
      as_tibble()
    
    DO_NEXT = dim(all_inspections[[loop_index]])[1] == chunk_size
    loop_index = loop_index + 1
  }
  
  all_inspections
  
}

url = "https://data.cityofnewyork.us/resource/43nn-pn8j.json"

nyc_inspections = 
  get_all_inspections(url) %>%
  bind_rows() 

# Select relevant columns and clean the data
cleaned_data <- nyc_inspections %>%
  select(boro, inspection_date, critical_flag, latitude, longitude, grade, grade_date) %>%
  filter(!is.na(boro) & !is.na(inspection_date) & !is.na(latitude) & !is.na(longitude) & !is.na(grade)) %>%
  mutate(inspection_date = ymd(inspection_date),
         grade_date = ymd(grade_date))

# Count of grades per borough
grade_boro_count <- cleaned_data %>%
  group_by(boro, grade) %>%
  summarise(count = n()) %>%
  ungroup()

# Prepare data for heatmap
heatmap_data <- cleaned_data %>%
  group_by(boro, grade) %>%
  summarise(count = n()) %>%
  ungroup()

Bar Plot

plot_ly(grade_boro_count, x = ~boro, y = ~count, color = ~grade, type = "bar") %>%
  layout(title = "Distribution of Inspection Grades by Borough",
         xaxis = list(title = "Borough"),
         yaxis = list(title = "Count"))

Scatter map plot for inspection locations

plot_ly(cleaned_data, x = ~longitude, y = ~latitude, type = 'scatter', mode = 'markers',
        color = ~grade, text = ~paste("Grade:", grade, "<br>Borough:", boro)) %>%
  layout(title = "Inspection Locations by Grade",
         xaxis = list(title = "Longitude"),
         yaxis = list(title = "Latitude"))

Heatmap

plot_ly(heatmap_data, x = ~boro, y = ~grade, z = ~count, type = "heatmap", color = "blue") %>%
  layout(title = "Heatmap of Grade Distribution by Borough",
         xaxis = list(title = "Borough"),
         yaxis = list(title = "Grade"))

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels